Dr. Johnnatan Messias

Abstract: This paper studies liquid staking tokens (LSTs) on automated market makers (AMMs), both theoretically and empirically. LSTs are tokenized representations of staked assets on proof-of-stake blockchains. First, we model LST-liquidity on AMMs theoretically, categorizing suitable AMM types for LST liquidity and deriving formulas for the necessary returns from trading fees to adequately compensate liquidity providers under the particular price trajectories of LSTs. For the latter, two relevant metrics are considered: (1) losses compared to holding the liquidity outside the AMM (loss-versus-holding, or "impermanent loss"), and (2) the relative profitability compared to fully staking the capital (loss-versus-staking) which is specifically tailored to the case of LST-liquidity. Next, we empirically measure these metrics for Ethereum LSTs across the most relevant AMM pools. We find that, while trading fees often compensate for impermanent loss, fully staking is more profitable for many pools, raising questions about the sustainability of the current LST liquidity allocation to AMMs.

Abstract: Arbitrage opportunities arise from the simultaneous buying and selling of the same asset in different markets to profit from price differences. This research systematically explores arbitrage possibilities between Automated Market Makers (AMMs) on Ethereum zk-rollups and Centralized Exchanges (CEXs). To start, we introduce a theoretical framework to assess arbitrage opportunities and develop a formula for the Maximal Arbitrage Value (MAV), considering both price discrepancies and the liquidity present in trading venues. Following this, we conduct an empirical assessment of the historical MAV between SyncSwap, an AMM on zkSync Era, and Binance, examining the speed at which price discrepancies are resolved considering both explicit and implicit market costs. Overall, the total MAV from July to September 2023 in the USDC-ETH SyncSwap pool amounts to $104.96K (0.24% of trading volume).

Abstract: Airdrops are used by blockchain applications and platforms to attract an initial user base, and to grow the user base over time. In the case of many airdrops, tokens are distributed to select users as a "reward" for interacting with the underlying platform, with a long-term goal of creating a loyal community that will generate genuine economic activity well after the airdrop has been completed. Although airdrops are widely used by the blockchain industry, a proper understanding of the factors contributing to an airdrop's success is generally lacking. In this work, we outline the design space for airdrops, and specify a reasonable list of outcomes that an airdrop should ideally result in. We then analyze on-chain data from several larger-scale airdrops to empirically evaluate the success of previous airdrops, with respect to our desiderata. In our analysis, we demonstrate that airdrop farmers frequently dispose of the lion's share of airdrops proceeds via exchanges. Our analysis is followed by an overview of common pitfalls that common airdrop designs lend themselves to, which are then used to suggest concrete guidelines for better airdrops.

Abstract: Smart contracts are contractual agreements between participants of a blockchain, who cannot implicitly trust one another. They are software programs that run on top of a blockchain, and we may need to change them from time to time (e.g., to fix bugs or address new use cases). Governance protocols define the means for amending or changing these smart contracts without any centralized authority. They distribute instead the decision-making power to every user of the smart contract: Users vote on accepting or rejecting every change. The focus of this work is to evaluate whether, how, and to what extent these protocols ensure decentralized governance, the fundamental tenet of blockchains, in practice. This evaluation is crucial as smart contracts continue to transform our key, traditional, centralized institutions, particularly banking and finance. In this work, we review and characterize decentralized governance in practice, using Compound -- one of the widely used governance protocols -- as a case study. We reveal a high concentration of voting power in Compound: 10 voters hold together 57.86% of the voting power. Although proposals to change or amend the protocol (or, essentially, the application they support) receive, on average, a substantial number of votes (i.e., 89.39%) in favor, they require fewer than three voters to obtain 50% or more votes. We show that voting on Compound governance proposals can be unfairly expensive for small token holders, and also discover voting coalitions that can further marginalize these users. We plan on publishing our scripts and data set on GitHub to support reproducible research.

Abstract: Despite blockchain data being publicly available, practical challenges and high costs often hinder its effective use by researchers, thus limiting data-driven research and exploration in the blockchain space. This is especially true when it comes to Layer 2 (L2) ecosystems, and ZKsync, in particular. To address these issues, we have curated a dataset from 1 year of activity extracted from a ZKsync Era archive node and made it freely available to external parties. In this paper, we provide details on this dataset and how it was created, showcase a few example analyses that can be performed with it, and discuss some future research directions. We also publish and share the code used in our analysis on GitHub to promote reproducibility and to support further research.

Abstract: This paper explores the dynamics of Decentralized Finance (DeFi) within the Layer-2 ecosystem, focusing on Automated Market Makers (AMM) and arbitrage on Ethereum rollups. We observe significant shifts in trading activity from Ethereum to rollups, with swaps on rollups happening 2-3 times more often, though, with lower trade volume. By examining the price differences between AMMs and centralized exchanges, we discover over 0.5 million unexploited arbitrage opportunities on rollups. Remarkably, we observe that these opportunities last, on average, 10 to 20 blocks, requiring adjustments to the LVR metrics to avoid double-counting arbitrage. Our results show that arbitrage in Arbitrum, Base, and Optimism pools ranges from 0.03% to 0.05% of trading volume, while in zkSync Era it oscillates around 0.25%, with the LVR metric overestimating arbitrage by a factor of five. Rollups offer not only lower gas fees, but also provide faster block production, leading to significant differences compared to the trading and arbitrage dynamics of Ethereum.

Abstract: Despite the level of attention given to rollups there is limited empirical research about their performance. To address this gap, we conduct a comprehensive data-driven analysis of the late 2023 transaction boom that is attributed to inscriptions: a novel approach to record data onto a blockchain with no outside server needed. Inscriptions were first introduced on the Bitcoin blockchain to allow for the representation of NFTs or ERC-20-like tokens without smart contracts, but were later spread to other blockchains. This work examines the applications of inscription transactions in Ethereum and its major EVM-compatible rollups and their impact on blockchain scalability during periods of sudden transaction surges. We found that on certain days, inscription-related transactions comprised over 89% on Arbitrum, over 88% on zkSync Era, and over 53% on Ethereum. Furthermore, 99% of these transactions were related to the minting of meme coins, followed by limited trading activity. Unlike L1 blockchains, during periods of transaction surges, zkSync and Arbitrum experienced lower median gas fees, attributable to the compression of L2 transactions for a single L1 batch. Additionally, zkSync Era, a ZK rollup, demonstrated a stronger reduction in fees than optimistic rollups considered in our study: Arbitrum, Base, and Optimism.

Abstract: In permissionless blockchains, transaction issuers include a fee to incentivize miners to include their transaction. To accurately estimate this prioritization fee for a transaction, transaction issuers (or blockchain participants, more generally) rely on two fundamental notions of transparency, namely contention and prioritization transparency. Contention transparency implies that participants are aware of every pending transaction that will contend with a given transaction for inclusion. Prioritization transparency states that the participants are aware of the transaction or prioritization fees paid by every such contending transaction. Neither of these notions of transparency holds well today. Private relay networks, for instance, allow users to send transactions privately to miners. Besides, users can offer fees to miners via either direct transfers to miners' wallets or off-chain payments---neither of which are public. In this work, we characterize the lack of contention and prioritization transparency in Bitcoin and Ethereum resulting from such practices. We show that private relay networks are widely used and private transactions are quite prevalent. We show that the lack of transparency facilitates miners to collude and overcharge users who may use these private relay networks despite them offering little to no guarantees on transaction prioritization. The lack of these transparencies in blockchains has crucial implications for transaction issuers as well as the stability of blockchains. Finally, we make our data sets and scripts publicly available.

Abstract: Most public blockchain protocols, including the popular Bitcoin and Ethereum blockchains, do not formally specify the order in which miners should select transactions from the pool of pending (or uncommitted) transactions for inclusion in the blockchain. Over the years, informal conventions or "norms" for transaction ordering have, however, emerged via the use of shared software by miners, e.g., the GetBlockTemplate (GBT) mining protocol in Bitcoin Core. Today, a widely held view is that Bitcoin miners prioritize transactions based on their offered "transaction fee-per-byte." Bitcoin users are, consequently, encouraged to increase the fees to accelerate the commitment of their transactions, particularly during periods of congestion. In this paper, we audit the Bitcoin blockchain and present statistically significant evidence of mining pools deviating from the norms to accelerate the commitment of transactions for which they have (i) a selfish or vested interest, or (ii) received dark-fee payments via opaque (non-public) side-channels. As blockchains are increasingly being used as a record-keeping substrate for a variety of decentralized (financial technology) systems, our findings call for an urgent discussion on defining neutrality norms that miners must adhere to when ordering transactions in the chains. Finally, we make our data sets and scripts publicly available.

Abstract: We study efficiency in a proof-of-work blockchain with non-zero latencies, focusing in particular on the (inequality in) individual miners' efficiencies. Prior work attributed differences in miners' efficiencies mostly to attacks, but we pursue a different question: Can inequality in miners' efficiencies be explained by delays, even when all miners are honest? Traditionally, such efficiency-related questions were tackled only at the level of the overall system, and in a peer-to-peer (P2P) setting where miners directly connect to one another. Despite it being common today for miners to pool compute capacities in a mining pool managed by a centralized coordinator, efficiency in such a coordinated setting has barely been studied. In this paper, we propose a simple model of a proof-of-work blockchain with latencies for both the P2P and the coordinated settings. We derive a closed-form expression for the efficiency in the coordinated setting with an arbitrary number of miners and arbitrary latencies, both for the overall system and for each individual miner. We leverage this result to show that inequalities arise from variability in the delays, but that if all miners are equidistant from the coordinator, they have equal efficiency irrespective of their compute capacities. We then prove that, under a natural consistency condition, the overall system efficiency in the P2P setting is higher than that in the coordinated setting. Finally, we perform a simulation-based study to demonstrate that even in the P2P setting delays between miners introduce inequalities, and that there is a more complex interplay between delays and compute capacities.

Abstract: Blockchains suffer from a well-known, non-trivial scalability problem: The low throughput (i.e., transactions committed per unit time) of blockchains when paired with the increasingly high volume of issued transactions leads to significant delays in transaction commit times. In a month-long investigation of Bitcoin, we reveal that congestion (i.e., when there exist more transactions than can be included in a block) is typical and that commit times exhibit a significant variance during periods of congestion. Although the fee-per-byte dequeuing policy is widely considered the "norm" for prioritizing transactions — and explaining how and when transactions are committed — we show that miners somehow delay a significant fraction of transactions. Such deviations undermine the utility of blockchains for ensuring a "fair" ordering that might be required for some applications.

Abstract: WhatsApp has revolutionized the way people communicate and interact. It is not only cheaper than the traditional Short Message Service (SMS) communication but it also brings a new form of mobile communication: the group chats. Such groups are great forums for collective discussions on a variety of topics. In particular, in events of great social mobilization, such as strikes and electoral campaigns, WhatsApp group chats are very attractive as they facilitate information exchange among interested people. Yet, recent events have raised concerns about the spreading of misinformation in WhatsApp. In this work, we analyze information dissemination within WhatsApp, focusing on publicly accessible political-oriented groups, collecting all shared messages during major social events in Brazil: a national truck drivers' strike and the Brazilian presidential campaign. We analyze the types of content shared within such groups as well as the network structures that emerge from user interactions within and cross-groups. We then deepen our analysis by identifying the presence of misinformation among the shared images using labels provided by journalists and by a proposed automatic procedure based on Google searches. We identify the most important sources of the fake images and analyze how they propagate across WhatsApp groups and from/to other Web platforms.

Abstract: WhatsApp is the most popular communication application in many developing countries such as Brazil, India, and Mexico, where many people use it as an interface to the web. Due to its encrypted and peer-to-peer nature feature, it is hard for researchers to study which content people share through WhatsApp at scale. In this demo paper, we propose WhatsApp Monitor (http://www.whatsapp-monitor.dcc.ufmg.br), a web-based system that helps researchers and journalists explore the nature of content shared on WhatsApp public groups from two different contexts: Brazil and India. Our tool monitors multiple content categories such as images, videos, audio, and textual messages posted on a set of WhatsApp groups and displays the most shared content per day. Our tool has been used for monitoring content during the 2018 Brazilian general election and was one of the major sources for estimating the spread of misinformation and helping fact-checking efforts.

Abstract: Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away from) certain perspectives. Given the considerable impact that search bias can have on the user, we propose a generalizable search bias quantification framework that not only measures the political bias in ranked list output by the search system but also decouples the bias introduced by the different sources — input data and ranking system. We apply our framework to study the political bias in searches related to 2016 US Presidential primaries in Twitter social media search and find that both input data and ranking system matter in determining the final search output bias seen by the users. And finally, we use the framework to compare the relative bias for two popular search systems — Twitter social media search and Google web search — for queries related to politicians and political events. We end by discussing some potential solutions to signal the bias in the search results to make the users more aware of them.

Abstract: Targeted advertising is meant to improve the efficiency of matching advertisers to their customers. However, targeted advertising can also be abused by malicious advertisers to efficiently reach people susceptible to false stories, stoke grievances, and incite social conflict. Since targeted ads are not seen by non-targeted and non-vulnerable people, malicious ads are likely to go unreported and their effects undetected. This work examines a specific case of malicious advertising, exploring the extent to which political ads from the Russian Intelligence Research Agency (IRA) run prior to 2016 U.S. elections exploited Facebook's targeted advertising infrastructure to efficiently target ads on divisive or polarizing topics (e.g., immigration, race-based policing) at vulnerable sub-populations. In particular, we do the following: (a) We conduct U.S. census-representative surveys to characterize how users with different political ideologies report, approve, and perceive truth in the content of the IRA ads. Our surveys show that many ads are "divisive": they elicit very different reactions from people belonging to different socially salient groups. (b) We characterize how these divisive ads are targeted to sub-populations that feel particularly aggrieved by the status quo. Our findings support existing calls for greater transparency of content and targeting of political ads. (c) We particularly focus on how the Facebook ad API facilitates such targeting. We show how the enormous amount of personal data Facebook aggregates about users and makes available to advertisers enables such malicious targeting.

Abstract: Social media is considered a democratic space in which people connect and interact with each other regardless of their gender, race, or any other demographic factor. Despite numerous efforts that explore demographic factors in social media, it is still unclear whether social media perpetuates old inequalities from the offline world. In this paper, we attempt to identify gender and race of Twitter users located in U.S. using advanced image processing algorithms from Face++. Then, we investigate how different demographic groups (i.e. male/female, Asian/Black/White) connect with other. We quantify to what extent one group follow and interact with each other and the extent to which these connections and interactions reflect in inequalities in Twitter. Our analysis shows that users identified as White and male tend to attain higher positions in Twitter, in terms of the number of followers and number of times in user's lists. We hope our effort can stimulate the development of new theories of demographic information in the online space.

Abstract: The widespread adoption and dissemination of online news through social media systems have been revolutionizing many segments of our society and ultimately our daily lives. In these systems, users can play a central role as they share content to their friends. Despite that, little is known about news spreaders in social media. In this paper, we provide the first of its kind in-depth characterization of news spreaders in social media. In particular, we investigate their demographics, what kind of content they share, and the audience they reach. Among our main findings, we show that males and white users tend to be more active in terms of sharing news, biasing the news audience to the interests of these demographic groups. Our results also quantify differences in interests of news sharing across demographics, which has implications for personalized news digests.

Abstract: The massive popularity of online social media provides a unique opportunity for researchers to study the linguistic characteristics and patterns of user's interactions. In this paper, we provide an in-depth characterization of language usage across demographic groups in Twitter. In particular, we extract the gender and race of Twitter users located in the U.S. using advanced image processing algorithms from Face++. Then, we investigate how demographic groups (i.e. male/female, Asian/Black/White) differ in terms of linguistic styles and also their interests. We extract linguistic features from 6 categories (affective attributes, cognitive attributes, lexical density and awareness, temporal references, social and personal concerns, and interpersonal focus), in order to identify the similarities and differences in particular writing set of attributes. In addition, we extract the absolute ranking difference of top phrases between demographic groups. As a dimension of diversity, we also use the topics of interest that we retrieve from each user. Our analysis unveils clear differences in the writing styles (and the topics of interest) of different demographic groups, with variation seen across both gender and race lines. We hope our effort can stimulate the development of new studies related to demographic information in the online space.

Abstract: Users of social media sites like Facebook and Twitter rely on crowdsourced content recommendation systems (e.g., Trending Topics) to retrieve important and useful information. Contents selected for recommendation indirectly give the initial users who promoted (by liking or posting) the content an opportunity to propagate their messages to a wider audience. Hence, it is important to understand the demographics of people who make a content worthy of recommendation, and explore whether they are representative of the media site's overall population. In this work, using extensive data collected from Twitter, we make the first attempt to quantify and explore the demographic biases in the crowdsourced recommendations. Our analysis, focusing on the selection of trending topics, finds that a large fraction of trends are promoted by crowds whose demographics are significantly different from the overall Twitter population. More worryingly, we find that certain demographic groups are systematically under-represented among the promoters of the trending topics. To make the demographic biases in Twitter trends more transparent, we developed and deployed a Web-based service "Who-Makes-Trends" at http://twitter-app.mpi-sws.org/who-makes-trends.

Abstract: On most online social media sites today, user-generated data remains accessible to allowed viewers unless and until the data owner changes her privacy preferences. In this paper, we present a large-scale measurement study focused on understanding how users control the longitudinal exposure of their publicly shared data on social media sites. Our study, using data from Twitter, finds that a significant fraction of users withdraw a surprisingly large percentage of old publicly shared data—more than 28% of 6-year old public posts (tweets) on Twitter are not accessible today. The inaccessible tweets are either selectively deleted by users or withdrawn by users when they delete or make their accounts private. We also found a significant problem with the current exposure control mechanisms—even when a user deletes her tweets or her account, the current mechanisms leave traces of residual activity, i.e., tweets from other users sent as replies to those deleted tweets or accounts still remain accessible. We show that using this residual information one can recover significant information about the deleted tweets or even characteristics of the deleted accounts. To the best of our knowledge, we are the first to study the information leakage resulting from residual activities of deleted tweets and accounts. Finally, we propose two exposure control mechanisms that eliminates information leakage via residual activities. One of our mechanisms optimize for allowing meaningful social interactions with user posts and another mechanism aims to control longitudinal exposure via anonymization . We discuss the merits and drawbacks of our proposed mechanisms compared to existing mechanisms.

Abstract: Sentiment Analysis has become a key tool to extract knowledge from data containing opinions and sentiments, particularly, data from online social systems. With the increasing use of smartphones to access social media platforms, a new wave of applications that explore sentiment analysis in the mobile environment is beginning to emerge. However, there are various existing sentiment analysis methods and it is unclear which of them are deployable in the mobile environment. In this paper, we provide the first of a kind study in which we compare the performance of 14 sentence-level sentiment analysis methods in the mobile environment. To do that, we adapted these methods to run on Android OS and then we measure their performance in terms of memory, CPU, and battery consumption. Our findings unveil methods that require almost no adaptations and run relatively fast as well as methods that could not be deployed due to excessive use of memory. We hope our effort provides a guide to developers and researchers interested in exploring sentiment analysis as part of a mobile application and can help new applications to be executed without the dependency of a server-side API. We also share the Android API that implements all the 14 sentiment analysis used in this paper.

Abstract: This large-scale measurement study of Twitter focuses on understanding how users control the longitudinal exposure of their publicly shared social data — that is, their tweets — and the limitations of currently used control mechanisms. Our study finds that, while Twitter users widely employ longitudinal exposure control mechanisms, they face two fundamental problems. First, even when users delete their data or account, the current mechanisms leave signficant traces of residual activity. Second, these mechanisms single out withdrawn tweets or accounts, attracting undesirable attention to them. To address both problems, an inactivity- based withdrawal scheme for improved longitudinal exposure control is explored.

Abstract: To help their users to discover the most interesting contents at a particular time, social media sites like Facebook and Twitter deploy content recommendation systems (such as Trending Topics), which often rely on crowdsourced popularity signals to select the contents. Once the contents are selected for recommendation, they reach a large population, effectively giving the initial users of the contents an opportunity to propagate their messages to the wider public. Hence, it is extremely important to understand the demographics of people who make a content worthy of recommendation, and explore whether there are demographic biases in the recommended contents where the majority of the recommended contents were initially popular with crowds exhibiting skewed demographic distributions.
In this work, using extensive data collected from Twitter, we make the first attempt to quantify and explore the demographic biases in the crowdsourced recommendations (particularly, in the selection of trending topics). In our analysis, we find that very different topics are popular among different demographic groups, and in practice, there is a bias towards a particular demographic while selecting the trending topics. We further propose and evaluate different techniques to limit such demographic biases in trending topic selection.

Abstract: Recently, there have been considerable efforts to use online data to investigate international migration. These efforts show that Web data are valuable for estimating migration rates and are relatively easy to obtain. However, existing studies have only investigated flows of people along migration corridors, i.e. between pairs of countries. In our work, we use data about "places lived" from millions of Google+ users in order to study migration "clusters", i.e. groups of countries in which individuals have lived. For the first time, we consider information about more than two countries people have lived in. We argue that these data are very valuable because this type of information is not available in traditional demographic sources which record country-to-country migration flows independent of each other. We show that migration clusters of country triads cannot be identified using information about bilateral flows alone. To demonstrate the additional insights that can be gained by using data about migration clusters, we first develop a model that tries to predict the prevalence of a given triad using only data about its constituent pairs. We then inspect the groups of three countries which are more or less prominent, compared to what we would expect based on bilateral flows alone. Next, we identify a set of features such as a shared language or colonial ties that explain which triple of country pairs are more or less likely to be clustered when looking at country triples. Then we select and contrast a few cases of clusters that provide some qualitative information about what our data set shows. The type of data that we use is potentially available for a number of social media services. We hope that this first study about migration clusters will stimulate the use of Web data for the development of new theories of international migration that could not be tested appropriately before.

Abstract: The increasing use of smartphones to access social media platforms opens a new wave of applications that explore sentiment analysis in the mobile environment. However, there are various existing sentiment analysis methods and it is unclear which of them are deployable in the mobile environment. This paper provides the first of a kind study in which we compare the performance of 17 sentence-level sentiment analysis methods in the mobile environment. To do that, we adapted these sentence-level methods to run on Android OS and then we measure their performance in terms of memory usage, CPU usage, and battery consumption. Our findings unveil sentence-level methods that require almost no adaptations and run relatively fast as well as methods that could not be deployed due to excessive use of memory. We hope our effort provides a guide to developers and researchers interested in exploring sentiment analysis as part of a mobile application and can help new applications to be executed without the dependency of a server-side API.

Abstract: On most online social media sites today, user-generated data remains accessible to allowed viewers unless and until the data owner changes her privacy preferences. In this paper, we present a large-scale measurement study focussed on understanding how users control the longitudinal exposure of their publicly shared data on social media sites. Our study, using data from Twitter, finds that a significant fraction of users withdraw a surprisingly large percentage of old publicly shared data -- more than 28% of six-year old public posts (tweets) on Twitter are not accessible today. The inaccessible tweets are either selectively deleted by users or withdrawn by users when they delete or make their accounts private. We also found a significant problem with the current exposure control mechanisms – even when a user deletes her tweets or her account, the current mechanisms leave traces of residual activity, i.e., tweets from other users sent as replies to those deleted tweets or accounts still remain accessible. We show that using this residual information one can recover significant information about the deleted tweets or even characteristics of the deleted accounts. To the best of our knowledge, we are the first to study th information leakage resulting from residual activities of deleted tweets and accounts. Finally, we propose an exposure control mechanism that eliminates information leakage via residual activities, while still allowing meaningful social interactions with user posts. We discuss its merits and drawbacks compared to existing mechanisms.

Abstract: Systems like Klout and Twitalyzer were developed as an attempt to measure the influence of users within social networks. Although the algorithms used by these systems are not public known, they have been widely used to rank users according to their influence, especially in the Twitter social network. As media companies might base their viral marketing campaigns on influence scores, users might attempt to boost their influence scores with simple mechanisms like following unknown users to be followed back or even interacting with those who reciprocate these actions. In this paper, we investigate if widely used influence scores are vulnerable and easy to manipulate. Our approach consists of developing Twitter bot accounts able to interact with real users to verify strategies that can increase their influence scores according to different systems. Our results show that it is possible to become influential using very simple strategies, suggesting that these systems should review their influence score algorithms to avoid accounting with automatic activity.

Abstract: In Brazil, 48% of the population use WhatsApp to share and discuss news. Currently, there are serious concerns that this platform can become a fertile ground for groups interested in disseminating misinformation, especially as part of articulated political campaigns. Particularly, WhatsApp provides an important space for users to engage in public conversations that worth attention, the public groups. These groups are suitable for political activism and social movement organization. Additionally, it is reasonable to assume that a malicious misinformation campaign might attempt to maximize the audience of a fake story by sharing it in existing public groups. In this paper, we present a system for gathering, analyzing and visualize public groups in WhatsApp. In addition to describe our methodology, we also provide a brief characterization of the content shared in 127 Brazilian groups. We hope our system can help journalists and researchers to understand the repercussion of events related to the Brazilian elections within these groups.

Abstract: Currently available data about people whose left their home country to live in a foreign country does not adequately capture the standards of contemporary global migration flows. A new trend for migration studies is to study the data from the Internet, either by Social Networks or other data in the WEB. In this study, we collected users data from the social network Google+ to investigate which features of Brazilian users are relevant to classify them as a possible emigrant. Our study uses machine learning techniques, SVM. We selected some features to compose our dataset. Our results show that the network features were the ones that had greater capacity for discrimination. The most relevant for the prediction of Brazilian emigrants users are, in order: reciprocity, PageRank, in-degree, clustering coefficient and ratio of incoming foreigners.

Abstract: This paper proposes using machine learning algorithms to predict the outcome of an MMA fight based on the characteristics of the two fighters and their recent opponents. Our experimental evaluation shows an approach to create a dataset applicable to individual sports and one of the evaluated algorithms has 67% of successful predictions.

Abstract: Sarcasm and irony are widely used forms of speech used inside and outside the Web, having the power to transform a sentence regarding its polarity or sense. The ability of characterizing and detecting sarcastic and ironic messages on data collected from Web could improve many decision-making systems based on Natural Language Processing (NLP) such as the sentiment analysis, text summarization and review ranking systems. In this work, we propose some approaches to the task of characterization and detection of sarcasm and irony in messages posted on Twitter online social network. Using an automatically collected dataset with the hashtags “#sarcasm” and“#irony”, and by exploiting a large set of characterization and classification techniques, our results show satisfactory rates of accuracy and Macro-F1.

Abstract: Systems that classify influential users in social networks has been used with great frequency, being referenced in scientific papers and the media as the ideal standard for evaluation of influence in the social network Twitter. We consider this measure a complex and subjective and therefore suspect vulnerability and ease of handling these systems. Based on this, we performed experiments and analyzes in two ranking systems of influence: Klout and Twitalyzer. We create simple robots capable of interacting through Twitter accounts and measure their influence. Our results show that it is possible to be influential through simple strategies. This suggests that the systems do not have ideal metric to rank influence.

Abstract: Blockchains revolutionized centralized sectors like banking and finance by promoting decentralization and transparency. In a blockchain, information is transmitted through transactions issued by participants or applications. Miners crucially select, order, and validate pending transactions for block inclusion, prioritizing those with higher incentives or fees. The order in which transactions are included can impact the blockchain final state.

Moreover, applications running on top of a blockchain often rely on governance protocols to decentralize the decision-making power to make changes to their core functionality. These changes can affect how participants interact with these applications. Since one token equals one vote, participants holding multiple tokens have a higher voting power to support or reject the proposed changes. The extent to which this voting power is distributed is questionable and if highly concentrated among a few holders can lead to governance attacks.

In this thesis, we audit the Bitcoin and Ethereum blockchains to investigate the norms followed by miners in determining the transaction prioritization. We also audit decentralized governance protocols such as Compound to evaluate whether the voting power is fairly distributed among the participants. Our findings have significant implications for future developments of blockchains and decentralized applications.

Abstract: Social media is considered a democratic space in which people connect and interact with each other regardless of their gender, race, or any other demographic aspect. Despite numerous efforts that explore demographic aspects in social media, it is still unclear whether social media perpetuates old inequalities from the offline world. In this dissertation, we attempt to identify gender and race of Twitter users located in the United States using advanced image processing algorithms from Face++. We investigate how different demographic groups (i.e. male/female, asian/black/white) connect with each other and differentiate them regarding linguistic styles and also their interests. We quantify to what extent one group follows and interacts with each other and the extent to which these connections and interactions reflect in inequalities in Twitter. We also extract linguistic features from six categories (affective attributes, cognitive attributes, lexical density and awareness, temporal references, social and personal concerns, and interpersonal focus) in order to identify the similarities and the differences in the messages they share in Twitter. Furthermore, we extract the absolute ranking difference of top phrases between demographic groups. As a dimension of diversity, we also use the topics of interest that we retrieve from each user. Our analysis shows that users identified as white and male tend to attain higher positions, in terms of the number of followers and number of times in another user's lists, in Twitter. There are clear differences in the way of writing across different demographic groups in both gender and race domains as well as in the topic of interest. We hope our effort can stimulate the development of new theories of demographic information in the online space. Finally, we developed a Web-based system that leverages the demographic aspects of users to provide transparency to the Twitter trending topics system.

Abstract: Autonomous unmanned flights undoubtedly enable new opportunities for scientific development. The drones can be used in military services, for example, in combat or as well as for rescue missions, aerial survey, supervision and inspection of a territory, attracting significant attention from media outlets such as, for example, television stations, radio, newspapers and internet. The goal of this project is whether it is possible to make viable autonomous flights at AR.Drone 2.0 and the understanding of its operation. This will require the implementation of a control program for autonomous flights. This framework requires the acquisition of data during the flight, which are obtained using sensors which use Arduino. The Arduino communication with the drone is needed for the inclusion of new sensors and the use of the AR.Drone is performed by the framework Node.js. Each remote button has a specific command, and may be in order for the user to create own missions or even perform some missions previously implemented by the developer. All tests were run on the AR.Drone 2.0, using the Node.js framework, sensors and a remote control. Through the experiments and presented studies became possible to achieve the proposed objective, making possible the implementation of autonomous flights in drone. As a result, for the realization of autonomous flight we designed a framework where the user can create autonomous flight missions for the drone run them. These commands are sent to the drone by the user due to use of a remote control. This remote control sends data to a sensor connected to the Arduino that processes the data and then is read and interpreted by the drone.